Sensor fusion for activity identification

ABSTRACT

A mobile device monitors accelerations using one or more inertial sensors. A user activity is identified based on the accelerations. A first estimation is made of a user activity statistic associated with the user activity based on the accelerations. Location information is obtained by one or more location based sensors. A second estimation is made of the user activity statistic based on the location information. The user activity statistic is calculated based on the first estimation and the second estimation.

FIELD OF THE INVENTION

This invention relates to monitoring human activity, and moreparticularly to accurately calculating user activity statistics using alocation based sensor and an inertial sensor.

BACKGROUND

The development of Micro-Electro-Mechanical Systems (MEMS) technologyhas enabled manufacturers to produce inertial sensors (e.g.,accelerometers) that have a small size, cost, and power consumption.Global positioning system (GPS) sensors have also been developed thatare of small size, cost and power consumption. Some manufacturers ofunmanned aerial vehicles (UAVs) have begun using navigation systems thatcombine sensor readings of inertial sensors and sensor readings of GPSsensors via sensor fusion to improve navigation. UAVs always perform thesame type of motions (flight in a forward direction). Therefore, thenavigation systems are not required to identify different types ofactivities. Nor are the navigation systems capable of performingactivity identification. Moreover, the navigation systems are alsoincapable of determining activity statistics associated with particularactivities.

Recent advances have enabled inertial sensors and GPS sensors to beinstalled in a limited number of mobile commercial electronic devicessuch as cellular phones and portable computers. However, no such mobiledevices are currently offered that perform sensor fusion to combine GPSsensor readings and inertial sensor readings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, and can be more fully understood with reference to thefollowing detailed description when considered in connection with thefollowing figures:

FIG. 1 is a block diagram illustrating an electronic device, inaccordance with one embodiment of the present invention;

FIG. 2 illustrates a block diagram of an activity monitoring system, inaccordance with one embodiment of the present invention;

FIG. 3 illustrates a flow diagram for a method of monitoring humanactivity using an inertial sensor and a location based sensor, inaccordance with one embodiment of the present invention;

FIG. 4 illustrates a flow diagram for a method of monitoring humanactivity using an inertial sensor and a location based sensor, inaccordance with another embodiment of the present invention;

FIG. 5A illustrates a flow diagram for a method of filtering useractivities based on location information, in accordance with oneembodiment of the present invention;

FIG. 5B illustrates a flow diagram for a method of filtering locationinformation based on acceleration measurements, in accordance with oneembodiment of the present invention;

FIG. 6 illustrates a flow diagram for a method of automaticallyinitiating actions based on location measurements, in accordance withone embodiment of the present invention; and

FIG. 7 illustrates a flow diagram for a method of planning a route, inaccordance with one embodiment of the present invention;

FIG. 8 illustrates a flow diagram for a method of tracking user progressalong a defined path, in accordance with one embodiment of the presentinvention;

FIG. 9 illustrates a flow diagram for a method of calibrating a stridelength correlation model, in accordance with one embodiment of thepresent invention; and

FIG. 10 illustrates a block diagram of a machine in the exemplary formof a computer system, in accordance with one embodiment of the presentinvention.

DETAILED DESCRIPTION

Embodiments of the present invention are designed to monitor humanactivity using multiple sensors. In one embodiment, a mobile devicemonitors accelerations using an inertial sensor. A user activity isidentified based on the accelerations. Examples of user activitiesinclude walking, running, rollerblading, bicycling, etc. A firstcalculation is made of a user activity statistic associated with theuser activity based on the accelerations. User activity statisticsinclude periodic human motion counts, distance traveled, location,calories burned, etc. Location information is obtained by a locationbased sensor. A second calculation is made of the user activitystatistic based on the location information. The final user activitystatistic is calculated based on the first calculation and the secondcalculation.

FIG. 1 is a block diagram illustrating an electronic device 100, inaccordance with one embodiment of the present invention. Electronicdevice 100 may be a cellular phone, wrist watch, mp3 player, personaldigital assistant (PDA), mobile game console, laptop computer, or anyother device which can support at least two sensors and be carried by auser.

In one embodiment, the electronic device 100 is a portable electronicdevice that includes one or more inertial sensors 135 and one or morelocation based sensors 165. The inertial sensor 135 may measureaccelerations along a single axis or multiple axes, and may measurelinear as well as rotational (angular) accelerations. In one embodiment,one or more inertial sensors together provide three dimensionalacceleration measurement data.

The inertial sensor 135 may generate acceleration measurement data 175continuously, or at a sampling rate that may be fixed or variable. Inone embodiment, the inertial sensor 135 receives a timing signal from atimer (not shown) to take measurements at the sampling rate.

The location based sensor 165 can include a single location basedsensor, or multiple location based sensors (e.g., multiple differenttypes of location based sensors). In one embodiment, the location basedsensor 165 includes a global positioning system (GPS) sensor, which mayinclude a GPS antenna and a GPS receiver. The GPS sensor obtainslocation information from one or more GPS satellites.

In one embodiment, the location based sensor 165 includes a networklocalization sensor. A network localization sensor determines a positionby receiving signals from multiple sources that have known locations,and calculating the position based on the combined signals usingtrigonometric relations. Network localization sensors may determinelocation based on trilateration, multilateration and/or triangulation.The signals used to determine location may be radio frequency (RF)signals formatted according to the Bluetooth protocol, Zigbee protocol,wireless fidelity (WiFi) protocol, global system for mobilecommunications (GSM) protocol, 3G mobile communications protocol, etc.For example, a first network localization sensor may perform networktriangulation using signals received from a mobile phone serviceprovider's cell towers. In another example, a second networklocalization sensor may perform triangulation using wireless fidelity(WiFi) signals received from multiple nearby WiFi access points (e.g.,hotspots).

In one embodiment, the location based sensor 165 includes a radiofrequency identification (RFID) reader that reads transponders (e.g.,passive integrated transponders (PITs)). Each transponder may report aspecific location. When, for example, a transponder that reportslocation A is read, the location based sensor 165 knows to a high degreeof certainty that the electronic device 100 is at location A.Alternatively, the location based sensor 165 may itself include a PITthat is read by an RFID reader at a known location. Upon the PIT beingread by a particular RFID reader having a known location, the locationbased sensor 165 may learn its current location.

Multiple location based sensors 165 may be used separately or together.When used separately, each location based sensor 165 may independentlydetermine a location of the electronic device 100, and report thelocation to a location processor 170. When used together, thecapabilities of one location based sensor 165 can be used to augment thecapabilities of another location based sensor 165. Examples of suchcooperative use of location based sensors 165 include assisted GPS andenhanced GPS, in which location data reported by a network localizationsensor is used to augment a GPS sensor. A single location may then bereported to the location processor 170.

The location based sensor 165 may generate location informationcontinuously, or at a sampling rate that may be fixed or variable. Inone embodiment, the location based sensor 165 receives a timing signalfrom a timer (not shown) to take measurements at the sampling rate. Thelocation based sensor 165 may obtain location measurements at a samplingrate that is the same as or different from the sampling rate at whichthe inertial sensor 135 collects acceleration measurement data.

The location based sensor (or location based sensors) 165 can report aposition of the electronic device 100 as a latitude and longitude, andmay report a horizontal accuracy. In one embodiment, the horizontalaccuracy of the location is reported as a confidence radius. Forexample, a location may be reported with a horizontal accuracy of 10 m,meaning that the reported location is accurate within a circle having a10 m radius. Accuracy of the location may vary from about 10 m to about300 m for location data obtained by a GPS sensor, depending on userlocation (e.g., in a city, under open sky, under a tree, in a building,etc.). The location information may further include an altitude, and mayinclude a vertical accuracy. The location information may also include atime that the location was recorded.

In one embodiment, the inertial sensor 135 is coupled to a motionprocessor 120. The motion processor 120 processes accelerationmeasurement data received from the inertial sensor 135 to identify useractivities. Examples of user activities that can be identified includewalking, running, rollerblading, bicycling, cross country skiing, andother repetitive motion-based activities. The motion processor 120 alsoestimates user activity statistics based on the acceleration measurementdata. User activity statistics may include multiple statisticsassociated with user activities (e.g., running and/or walking). Examplesof user activity statistics include data about recent workouts, distancetraveled per workout, distance traveled per day, average speed, highestspeed, average incline of surface traveled, etc.

The motion processor 120 may identify a current user activity from aplurality of identifiable user activities. In one embodiment, the motionprocessor 120 identifies a user activity by monitoring for differentevents, each event indicative of a different type of activity. Eventsoccur when certain motion criteria are satisfied (e.g., when a motionsignature indicative of a step occurs within a certain period). In oneembodiment, when enough events indicative of a particular user activityare detected, the motion processor 120 identifies the activity as beingperformed by the user. In one embodiment, events may include positiveevents (ones that must be met to classify a motion in a certain way) andnegative events (ones that indicate that a motion cannot be classifiedcertain way).

Once the motion processor 120 has identified a user activity, the motionprocessor 120 may apply a set of motion criteria specific to theidentified activity to estimate one or more user activity statistics(e.g., to detect appropriate periodic human motions). Motion criteriamay include acceleration thresholds (e.g., a step may be counted if themeasured acceleration is below a first threshold and/or above a secondthreshold), acceleration comparison requirements (e.g., a step may becounted if a current measurement of acceleration is above or below oneor more previous measurements of acceleration), cadence windows (e.g., astep may be counted if accelerations characteristic of a step occurwithin a certain timeframe as measured from a previous step), etc.

One type of user activity statistic that the motion processor 120 candetermine is a number of steps (or other periodic human motions) taken.In one embodiment, a series of motion criteria are applied to theacceleration measurement data to detect steps or other periodic humanmotions. If each of the motion criteria are satisfied, a step may beidentified, and counted. Alternatively, if a sufficient number of themotion criteria are satisfied, without a number of negative events, astep may be counted. In one embodiment, a different set of motioncriteria may apply for running, for walking, and/or for other periodichuman motions. For example, a first threshold and first cadence windowmay be used to determine if a step has occurred while a user is running,and a second threshold and second cadence window may be used todetermine if a step has occurred while a user is walking.

Another type of user activity statistic that can be determined by themotion processor 120 is a distance traveled. A user's stride length canbe determined for a step based on gait characteristics associated withthe step. Examples of gait characteristics include step cadence, heelstrike, and other gait characteristics that can be derived fromacceleration measurements. For example, if a step cadence of 70 stepsper minute and a specific heel strike are detected, a stride length of 2ft. may be determined. Step detection and the calculated stride lengthof each step can then be used to calculate distance traveled.

In one embodiment, the stride length is determined by comparing gaitcharacteristics to a stride length correlation model. The stride lengthcorrelation model correlates stride lengths to steps (or other periodichuman motions) based on gait characteristics associated with the step(e.g., step cadence, heel strike, and other gait characteristics thatcan be derived from acceleration measurements). In one embodiment, thestride length correlation model includes a stride length algorithm thatidentifies a specific stride length when one or more gaitcharacteristics are used as input. The stride length algorithm may varydepending on user attributes (e.g., depending on user weight, height,athletic ability, etc.).

In one embodiment, the stride length correlation model includes a stridelength data structure (e.g., a lookup table, tree, etc.) that has acollection of entries. Each entry may associate a specific set of gaitcharacteristics with a specific stride length. For example, the datastructure may include a first entry associating a cadence of 70 stepsper minute with a stride length of 2.6 feet, a second entry associatinga cadence of 100 steps per minute with a stride length of 3 feet, etc.Each entry in the data structure may be associated with a range of gaitcharacteristics. Therefore, for example, the cadences of 5-10 steps perminute may all be associated with a stride length of 2 feet in an entry.The use of a data structure may require less computing power than theuse of the algorithm. The greater the number of entries, the moreaccurate the data structure, but the more memory it may require.

Other user activity statistics that the motion processor 120 canestimate include calories burned, route traveled, average speed oftravel, maximum speed of travel, workout intensity, vertical distancetraveled, and so on. These user activity statistics may be determinedbased on knowledge of user attributes (e.g., user weight, height, age,athletic ability, etc.), current user activity, and accelerationmeasurement data.

In one embodiment, steps (or other periodic human motions) may beaccurately counted, and speed and distance traveled may be accuratelydetermined by the motion processor 120, regardless of the placementand/or orientation of the electronic device 100 on a user. In a furtherembodiment, steps may be accurately counted, and speed and distance maybe accurately determined, whether the electronic device 100 maintains afixed orientation or changes orientation during use. In one embodiment,this can be achieved by monitoring the acceleration measurements todetermine a dominant axis (axis that is most affected by gravity), andidentifying a relationship between the axes of the inertial sensor andan orientation of the user based on the dominant axis.

In one embodiment, the motion processor 120 determines an accuracy ofthe calculated user activity statistics, as well as a confidence for thedetermined user activity. The accuracy can be determined based on theregularity (e.g., repeatability) of the user's motions, a number ofpositive events and negative events that are identified, motion criteriathat are satisfied, etc. In one embodiment, the accuracy/confidence ofthe user activity and the user activity statistics are reported as apercentage of certainty. For example, the motion processor 120 maydetermine that there is a 94% likelihood that the user is walking at acadence of 25 steps per minute. Alternatively, the confidence may bereported as a standard deviation, probability distribution, etc. In oneembodiment, the motion processor 120 can report multiple useractivities, and a likelihood that each is being performed. For example,the motion processor 120 may determine that there is a 35% likelihoodthat the user is running and a 65% likelihood that the user is speedwalking.

In one embodiment, the map is used to plan a path between a startinglocation and an ending location. Path planning is described in greaterdetail below with reference to FIG. 7. In one embodiment, the map isused to direct a user through a path and/or to provide instructions tothe user as the user traverses the path. Such an embodiment is describedin greater detail below with reference to FIG. 8.

Referring to FIG. 1, in one embodiment, the location based sensor 165 iscoupled to a location processor 170. The location processor 170processes location measurement data received from the location basedsensor 165. Based on the location information, the location processor170 estimates user activity statistics such as speed of travel, routetraveled, etc.

In one embodiment, the location processor 165 receives locationinformation from multiple location based sensors 165. The locationprocessor 170 may determine which of the location based sensor 165 isproviding the most accurate location information, and use that locationinformation to estimate user activity statistics. In one embodiment, thelocation processor 170 determines an accuracy of each of the useractivity statistics that it estimates. The accuracy of the user activitystatistics may be determined based on an accuracy of the locationinformation reported by the location based sensor 165.

In one embodiment, the location processor 170 includes a map (or hasaccess to a map, such as from a web mapping service). The user locationmay be compared to the map to determine the type of terrain that theuser is traveling on. For example, the location processor 170 maydetermine that the user is traveling on a paved sidewalk, on a dirttrail, on a track, in a building, etc.

In one embodiment, the location processor 170 automatically performs anaction when a predetermined location is identified. The action may beinitiating a run application, or another appropriate action. Thepredetermined locations may be configured by a user. Additionally, somepredetermined locations may be set by default. For example, gymlocations may be configured by default so that a sports trainingapplication is automatically launched if a user location matches alocation of a gym. In one embodiment, locations of particular items oftraining equipment within a gym can also be set. These locations may beidentified, for example, using an RFID reader. The electronic device 100can then determine information about the training equipment on which auser is training to more accurately log a workout. Alternatively, theuser may indicate such locations. Alternatively a map of the gym layoutmay be acquired, which specifies such locations.

In one embodiment, the location processor 170 can set the predeterminedlocation and/or the action based on repeated user behavior. For example,if the user typically starts a run at a first location and stops the runat a second location, these locations may be automatically recorded intomemory, and associated with a run application. Of course, as at a track,the first and the second location may be the same location. The runapplication may then automatically be started when the user is locatedat the first location, and may automatically be terminated when the useris located at the second location. Therefore, the user may perform arun, and have the run recorded, without having to manually launch a runapplication.

In one embodiment, the motion processor 120 is connected with thelocation processor 170. The location processor 170 may send locationinformation and/or estimated user activity statistics (includingestimated accuracies) to the motion processor 120. The motion processor120 may compare the received data to the user activity and/or the useractivity statistics estimated by the motion processor 120. If thereceived data has a high accuracy and the user activity and/or estimateduser activity statistic calculated by the motion processor 120 have alow accuracy, the motion processor 120 may determine a new user activityand/or estimate a new user activity statistic. For example, if themotion processor 120 had originally determined that the user activity iswalking, and the location processor 170 reports with a high degree ofaccuracy that the user is traveling at a speed that exceeds a user'smaximum walking speed, the motion processor 120 may determine that theuser is running, or that the user is riding in a car.

In one embodiment, if the location information (GPS-based, networktriangulation, etc.) has a high accuracy, and the user activity and/oruser activity statistics calculated by the motion processor have a lowaccuracy, the motion processor uses the location information to performcalibration. In one embodiment, the motion processor 120 calibrates astride length correlation model using the location information.

In one embodiment, motion processor 120 calibrates a stride lengthcorrelation model based on received location information that identifiesa distance traveled. Such received location information may becorrelated to gait characteristics that were collected while thereceived distance was walked or run. This correlation may then becompared to the stride length correlation model. If the correlationbased on the received location information does not match the stridelength correlation model, then the stride length correlation model maybe modified or replaced.

In one embodiment, calibrating the stride length correlation modelincludes generating a new data structure using a data structuregeneration algorithm. The data structure generation algorithm may use asinputs user attributes (e.g., height, weight, age, etc.) and/or a stridelength vs. gait characteristic correlation determined based on receiveddistance information. Therefore, a new data structure can be producedthat is tailored to user attributes of a specific user and/or based onempirical data taken of the specific user. Each entry in the new datastructure may be more accurate for the user than entries in anuncalibrated data structure.

In another embodiment, calibrating the stride length correlation modelincludes adjusting entries in an existing data structure. Suchadjustments may include shifting entries (e.g., adjusting entry valuesup or down), compressing entries (e.g., causing entries to represent asmaller range of gait characteristics), stretching entries (e.g.,causing entries to represent a greater range of gait characteristics),scaling entries (e.g., multiplying entries by a percentage or scalingfactor), etc. Adjustments may be made based on one or more of userattributes and a stride length vs. gait characteristic correlationdetermined based on received distance information. For example, a globalshift may be applied to entries if a user walked 1 mile, but a distanceof 1.5 miles was measured. Such a global shift could include shiftingdown the entries to reflect that the actual stride length is shorterthan represented by the data structure. Alternatively, if only a fewentries in the data structure are off, then only those entries may beshifted.

In yet another embodiment, calibrating the stride length correlationmodel includes modifying a stride length algorithm. Constants and/orvariables that apply to user attributes may be modified. Moreover,adjustments may be made to the algorithm based on a stride length vs.gait characteristic correlation determined based on received distanceinformation.

Calibration logic 220 may further calibrate an incline adjustment factor(not shown). The incline adjustment factor may be applied to the stridelength correlation data when an incline is detected. For example, when auser walks uphill, the user is likely to take smaller steps than whenthat user walks on level terrain. This difference in stride length maybe accounted for using the incline adjustment factor. A value of theincline adjustment factor that is applied to the stride lengthcorrelation model may depend on a degree of incline and on userattributes. The incline adjustment factor may be calibrated in themanners discussed above with reference to the stride length correlationmodel.

The motion processor 120 may also send estimated user activitystatistics to the location processor 170. The location processor 170 maycompare the received user activity statistics to user activitystatistics estimated by the location processor 170 and/or to locationinformation received from the location based sensor 165. If the receiveduser activity statistics have a high accuracy, the location processor170 may use the received user activity statistics to determine that thelocation based sensor 165 has not locked in on a GPS signal yet, or tolower the accuracy rating of the location information from the locationbased sensor 165.

In one embodiment, the motion processor 120 and location processor 170are connected with a calculating logic 185. The estimated user activitystatistics determined by each of the location processor 170 and themotion processor 120 are reported to the calculating logic 185, alongwith confidence interval of the estimations. The calculating logic 185combines the two estimated user activity statistics to calculate moreaccurate final user activity statistics. The calculating logic 185 canuse information from the motion processor 120 and the location processor170 in conjunction to accurately count steps, determine speed of travel,determine distance traveled, etc.

In one embodiment, the calculating logic 185 applies a weight to each ofthe received user activity statistic estimations based on the reportedaccuracy of the estimations. The reported accuracy may range from around1% accuracy to around 99% accuracy. In example, if the locationprocessor 170 generated an estimation for a distance traveled that was10 miles, with an 80% accuracy, and the motion processor 120 generated adistance traveled estimation of 11 miles with a 90% accuracy, theseestimations may be combined as follows: 8/17(10)+ 9/17(11)=10.26 miles.Alternatively, other algorithms may be used to combine the user activitystatistics. For example, some user activity statistic estimations of themotion processor 120 may be weighted more heavily than those with anequivalent accuracy rating from the location processor 170. In oneembodiment, if an accuracy rating of a user activity statistic estimatedby a processor falls below a threshold, the estimation associated withthe accuracy rating is not used at all. In such an instance, thecalculating logic 185 may completely rely upon the estimation of theother processor(s).

In one embodiment, the location based sensor 165, inertial sensor 135and calculating logic 185 are connected with a memory 110. Memory 110may include one or more of volatile memory (e.g., random access memory(RAM)), nonvolatile memory (e.g., read only memory (ROM), or flashmemory), a hard disk drive, an optical drive, etc. Memory 110 maycomprise multiple memories, i.e. a RAM memory used as a buffer, and aflash memory used to store other data. The location based sensor 165 andinertial sensor 135 store sensor data 175 (e.g., accelerationmeasurement data and location information) in memory 110. In oneembodiment, a buffer may collect acceleration measurement data and/orlocation information, and the buffered data may be used by motionprocessor 120 and/or location processor 170 for their calculations. Insuch an embodiment, motion processor 120 and/or location processor 170may also be connected to memory 110. In one embodiment, once thecalculating logic 185 calculates user activity statistics 180, it storesthem in memory 110.

In one embodiment, motion processor 120, location processor 170 andcalculating logic 185 are logics executed by a microcontroller 115,field programmable gate array (FPGA), application specific integratedcircuit (ASIC), or other dedicated processing unit. In anotherembodiment, one or more of the motion processor 120, location processor170 or calculating logic 185 may be logics executed by a centralprocessing unit. Alternatively, one or more of the motion processor 120,location processor 170 or calculating logic 185 may include a statemachine (e.g., an internal logic that knows how to perform a sequence ofoperations), a logic circuit (e.g., a logic that goes through a sequenceof events in time, or a logic whose output changes immediately upon achanged input), or a combination of a state machine and a logic circuit.

FIG. 2 illustrates a block diagram of an activity monitoring system 200,in accordance with one embodiment of the present invention. The activitymonitoring system 200 may monitor the activity of one or more usersconnected thereto. Records of such user activity may be maintained bythe activity monitoring system 200. In one embodiment, the activitymonitoring system 200 includes a mobile device 205 and a server 210.

In one embodiment, the mobile device 205 operates in conjunction withthe server 210 to determine step count, speed of travel, distancetraveled, and/or other user activity statistics. In the illustratedembodiment, mobile device 205 and server 210 are connected via a network210, which may be a public network (e.g., internet) or private network(e.g., local area network (LAN), intranet, etc.). Alternatively,connections may be established directly between server 210 and mobiledevice 205. Wireless connections may be established (directly betweendevices or via network 215) using any wireless data transfer protocol,such as Bluetooth, radiofrequency (RF), wireless local area network(WLAN), infrared, cellular networks including global system for mobilecommunications (GSM), code division multiple access (CDMA), integrateddigital enhanced network (iDEN), etc. Wired connections may beestablished using firewire, Ethernet, universal serial bus (USB), etc.

Mobile device 205 may be a mobile phone, PDA, pedometer, etc. In oneembodiment, the mobile device 105 corresponds to electronic device 100of FIG. 1. In a further embodiment, the mobile device 205 includes amotion processor 220 and a location processor 225. The motion processor220 and location processor 225 in one embodiment operate as described inFIG. 1 with reference to location processor 170 and motion processor120.

Motion processor 220 and location processor 225 are connected to a localcalculating logic 230. The local calculating logic 230 receives useractivity statistic estimations from the motion processor 220 andlocation processor 225, and calculates user activity statistics from theestimations on the fly (in real time). In the context of a sportsapplication, this enables a user to be constantly apprised of his or herworkout progress. However, in mobile devices 205 that have limitedprocessing power, and/or that can dedicate only minimal processing powerto the motion processor 220, location processor 225 and localcalculating logic 230, the calculated user activity statistics may havereduced accuracy. To improve the accuracy of calculated user activitystatistics, user activity statistics may be calculated by server 210 inone embodiment.

Server 210 may be a personal computer (desktop or laptop), networkserver, game kiosk, etc. Server 210 may receive user activitystatistics, acceleration measurement data, user characteristics, etc.from the mobile device 205 directly or via the network 215. In oneembodiment, server 210 includes a remote calculating logic 235. Remotecalculating logic 235 uses the received user activity statistics,acceleration measurement data, user characteristics, etc. to recalculatethe user activity statistics. The server 210 may have more processingpower than mobile device 205, and may calculate the user activitystatistics to a higher level of accuracy. In one embodiment, remotecalculating logic 235 calculates user activity statistics, and transmitsthe calculated user activity statistics to the mobile device pseudo-realtime. Such transmissions and calculations may be made pseudo-real timewhere a connection of sufficient bandwidth (a connection in whichconstant acceleration data, location data and user activity statisticscan be transmitted with minimal lag) is established between the mobiledevice 205 and the server 210. In another embodiment, the remotecalculating logic 235 recalculates the user activity statistics after auser activity has ended (e.g., after the user has finished a run). Theserver 210 can reconstruct an entire user event/activity after the factwith increased accuracy.

FIG. 3 illustrates a flow diagram for a method 300 of monitoring humanactivity using an inertial sensor and a location based sensor, inaccordance with one embodiment of the present invention. The method maybe performed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. In one embodiment, method 300 is performed by theelectronic device 100 of FIG. 1.

Referring to FIG. 3, method 300 includes monitoring accelerations (block305). Monitoring accelerations may include obtaining accelerationmeasurement data from one or more inertial sensors and/or otheracceleration monitoring devices. At block 310, a motion processorprocesses the accelerations to identify a user activity. Examples ofuser activities that can be identified include walking, running,bicycling, rollerblading, etc. In one embodiment, the user activity isidentified by monitoring for different events, each event indicative ofa different type of activity. When enough events indicative of aparticular user activity are detected, the user activity may beidentified.

At block 315, the motion processor makes a first estimation of a useractivity. The user activity statistic may be estimated based on theacceleration measurement data. User activity statistics may includemultiple statistics associated with user activities (e.g., runningand/or walking). Examples of user activity statistics include data aboutrecent workouts, distance traveled per workout, distance traveled perday, average speed, highest speed, average incline of surface traveled,etc. User activity statistics may be identified by applying a set ofmotion criteria specific to the identified activity. Motion criteria mayinclude acceleration thresholds (e.g., a step may be counted if themeasured acceleration is below a first threshold and/or above a secondthreshold), acceleration comparison requirements (e.g., a step may becounted if a current measurement of acceleration is above or below oneor more previous measurements of acceleration), cadence windows (e.g., astep may be counted if accelerations characteristic of a step occurwithin a certain timeframe as measured from a previous step), etc.

At block 318, a first accuracy of the first estimation is determined.The first accuracy may be in the form of a percentage of accuracy, astandard deviation, or other confidence rating.

At block 320, a location processor obtains location information using alocation based sensor. Examples of location based sensors include a GPSsensor, a network localization sensor, a transponder, etc. At block 325,the location processor makes a second estimation of the user activitystatistic based on the location information. At block 330, the locationprocessor determines accuracy for the second estimation.

At block 335, a calculating logic weights the first estimation and thesecond estimation based on the first accuracy and the second accuracy.The calculating logic may be a local calculating logic (e.g., thatoperates on a mobile device on which the location processor and motionprocessor operate) or a remote calculating logic (e.g., that is hostedby a server). At block 340, the calculating logic calculates the useractivity statistic based on the first estimation and the secondestimation. The user activity statistic may be calculated on the fly, orafter a user activity has ended. If this process was calculated at theend of the workout, the method then ends. If the process was calculatedwhile the workout was still going on, the process returns to block 315to continue monitoring.

FIG. 4 illustrates a flow diagram for a method 400 of monitoring humanactivity using an inertial sensor and a location based sensor, inaccordance with another embodiment of the present invention. The methodmay be performed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. In one embodiment, method 400 is performed by themonitoring system 200 of FIG. 2.

Referring to FIG. 4, method 400 includes monitoring accelerations by amobile device (block 405). Monitoring accelerations may includeobtaining acceleration measurement data from one or more inertialsensors and/or other acceleration monitoring devices. At block 410, themobile device obtains location information using a location basedsensor. At block 415, the accelerations and the location information aretransmitted to an external computing device (e.g., a server). In oneembodiment, the location information and accelerations are transmittedwirelessly (e.g., via WiFi, GSM, 3G, Bluetooth, etc.).

At block 420, it is determined whether a user activity has ended. Such adetermination may be made by the mobile device or by the externaldevice. If the user activity has ended, the method proceeds to block430. If the user activity has not ended, the method proceeds to block425.

At block 425, the mobile device receives a user activity statistic thathas been calculated by the external computing device. In one embodiment,this user activity statistic may be displayed to the user. The methodthen returns to block 405 to continue monitoring for accelerations.

At block 430, the mobile device receives a calculated final activitydata, or reconstruction, of an event that includes multiple useractivity statistics. The event may be a workout session (e.g., a run),and may include, for example, number of laps, distance traveled, routetraveled, number of steps, average running speed, and so on. The finalactivity data may alter the activity data that was displayed to the userduring the work-out. However, the final activity data will be moreaccurate, in one embodiment, since it will include data from multiplesensors.

FIG. 5A illustrates a flow diagram for a method 500 of filtering useractivities based on location information, in accordance with oneembodiment of the present invention. The method may be performed byprocessing logic that may comprise hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), or a combination thereof. Inone embodiment, method 500 is performed by the electronic device 100 ofFIG. 1.

Referring to FIG. 5A, method 500 includes monitoring accelerations usingan inertial sensor housed in a mobile device (block 505). At block 510,a motion processor processes the accelerations to identify a useractivity. At block 515, a location processor obtains locationinformation using a location based sensor. In one embodiment, thelocation information is a latitude and longitude. In one embodiment, alocation processor utilizes map data to map the latitude and longitudeto a map location.

At block 525, the accuracy of the user activity is verified using thelocation information. The mobile device can determine, for example,whether a user location has changed too quickly for the identified useractivity. For example, if the user is moving at a speed of around 50mph, it can be determined that the user is not walking. For anotherexample, if the user is jogging at a steady pace, but the location dataindicates that the user is travelling through various buildings, thereis a mismatch between the user activity and the map locationinformation. If the location information excludes the identified useractivity, the method proceeds to block 530, and a new user activity isidentified. In another embodiment, the accuracy rating of the useractivity and/or location information is changed due to the mismatch.

If the location information does not exclude the calculated useractivity, the method ends.

FIG. 5B illustrates a flow diagram for a method 550 of filteringlocation information based on acceleration measurements, in accordancewith one embodiment of the present invention. The method may beperformed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. In one embodiment, method 550 is performed by theelectronic device 100 of FIG. 1.

Referring to FIG. 5B, method 550 includes monitoring accelerations usingan inertial sensor housed in a mobile device (block 555). At block 560,a motion processor processes the accelerations to identify a useractivity.

At block 565, a location processor obtains location information using alocation based sensor. At block 570, the location processor obtains newlocation information using the location based sensor. At block 575, thelocation processor compares the change in location to the user activityand/or the accelerations. If the user activity correlates to the changein location, the method ends. If the user activity does not correlate tothe change in location, the method proceeds to block 585, and thelocation information is disregarded. The method then ends.

FIG. 6 illustrates a flow diagram for a method 600 of automaticallyinitiating actions based on location measurements, in accordance withone embodiment of the present invention. The method may be performed byprocessing logic that may comprise hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), or a combination thereof. Inone embodiment, method 600 is performed by the electronic device 100 ofFIG. 1.

Referring to FIG. 6, method 600 includes obtaining location informationusing a location based sensor (block 605). At block 610, a locationprocessor determines whether a current location matches a location basedtrigger. The location based trigger may have been programmed by a user,or may have been automatically determined based on repeated userbehavior. For example, if a user is detected to repeatedly begin a runat a particular location, a run application may be automaticallyinitiated when the user is detected to be at the particular location. Ifthe current location matches a location based trigger, the methodproceeds to block 615. Otherwise, the method ends.

At block 615, a preconfigured action is automatically performed (e.g.,starting a run application). The method then ends.

FIG. 7 illustrates a flow diagram for a method 700 of planning a route,in accordance with one embodiment of the present invention. The methodmay be performed by processing logic that may comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. In one embodiment, method 700 is performed by theelectronic device 100 of FIG. 1.

Referring to FIG. 7, at block 715, an electronic device receives astarting point and an end point from a user. At block 720, theelectronic device receives a route length from the user. At block 722,the method generates one or more possible paths between the startingpoint and the end point.

At block 725, the method determines whether any constraints have beenplaced on paths between the starting point and the ending point.Examples of constraints include number of turns, amount of verticaldistance traveled, preferred types of terrain to travel over, estimatedtime to completion, and so on. Constraints may also include specificwaypoints. For example, a user may indicate that the path should passthrough a first location and a second location. The user may furtherindicate that the path should pass through the first location beforepassing through the second location. If one or more constraints areplaced on the path, the method proceeds to block 735. If no constraintsare placed on the path, the method proceeds to block 765. In oneembodiment, the possible paths are generated before a user has inputconstraints. Alternatively, the user may input constraints before themethod generates possible paths.

At block 765, possible paths are displayed to a user. The method thenproceeds to block 748.

At block 735, the method determines whether there is at least one paththat meets all constraints that have been specified by a user. If thereis a path that meets all constraints, the method proceeds to block 745.If no path meets all constraints, the method proceeds to block 740.

At block 740, user paths are displayed in an order of importance. Thosepaths that meet the most constraints are shown first (e.g., at the topof a list), while those paths that meet the fewest constraints are shownlast, or not shown at all. In one embodiment, the constraints are eachassociated with an importance value. The importance value indicates howimportant it is to a user that a particular constraint be met. Thosepaths that meet constraints that have a high importance value appear aredisplayed before those that meet constraints that have a lowerimportance value. The importance value may be input by the user, or maybe preconfigured.

At block 745, paths that meet all constraints are displayed. In oneembodiment, paths that do not meet all constraints are also displayed.Paths that do not meet constraints may be displayed as discussed withreference to block 740. The method proceeds to block 748.

At block 748, a user selection of a path is received. At block 750, themethod determines whether the user selected a path that failed to meetall constraints. If the user did not choose a path that failed to meetall constraints (e.g., if there were no constraints, or if allconstraints were met), the method ends. If the user chose a path thatfailed to meet all constraints, the method continues to block 755, andthe importance value of the constraints that were not met are loweredfor future evaluations. The method then ends.

FIG. 8 illustrates a flow diagram for a method 800 of tracking userprogress along a defined path, in accordance with one embodiment of thepresent invention. The method may be performed by processing logic thatmay comprise hardware (e.g., circuitry, dedicated logic, programmablelogic, microcode, etc.), software (such as instructions run on aprocessing device), or a combination thereof. In one embodiment, method800 is performed by the electronic device 100 of FIG. 1.

Referring to FIG. 8, at block 820, the method tracks user location. Userlocation may be tracked using one or more location based sensors.

At block 825, the method determines whether the user is following adefined path. The defined path may be a track, trail, street, or otherphysically discernable path. The method may determine that the user isfollowing such a defined path by comparing the user location to a map.The defined path may also be a predetermined path that has been selectedby the user. For example, the user may input a destination, and adefined path may be determined that will lead the user from a currentlocation or input starting location to the input destination. Such apredetermined path may not correspond to a physically discernible pathsuch as a trail or street. If the user is following a defined path, themethod proceeds to block 830. Otherwise the method ends.

A block 830, the method provides directions to the user. The directionsmay direct the user, for example, to change direction (e.g., turn rightor left) at a specific location. The directions may be provided to theuser to keep the user on the defined path. If the user veers from thedefined path, the instructions may guide the user back to the definedpath.

At block 835, the method determines whether the user as arrived at anend of the defined path. If the user has arrived at the end of thedefined path, the method ends. Otherwise, the method proceeds to block840.

At block 840, the method determines whether the user has reached awaypoint. The waypoint may be a predetermined location that causes themethod to perform an action. Examples of actions include providingwaypoint data, performing calibration, playing music, etc. If the userhas not reached a waypoint, the method returns to block 820. If the userhas reached a waypoint, the method proceeds to block 845.

At block 845, the method provides waypoint data. The waypoint data mayinclude current location, user statistics associated with a current run(e.g., average speed, distance traveled, distance remaining in definedpath, etc.), and so on. The method then returns to block 820, andcontinues to track user location.

FIG. 9 illustrates a flow diagram for a method 900 of calibrating astride length correlation model, in accordance with one embodiment ofthe present invention. In one embodiment, the method calibrates thestride length correlation model during a user activity. In anotherembodiment, the method calibrates the stride length correlation modelafter a user activity ends. The method may be performed by processinglogic that may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (such as instructions runon a processing device), or a combination thereof. In one embodiment,method 900 is performed by the electronic device 100 of FIG. 1.

Referring to FIG. 9, at block 925, the method determines whether a userhas completed a portion of a defined path. The defined path may havebeen generated as described with reference to FIG. 7. If the user hasnot completed a portion of the defined path, the method returns to block925 and again determines whether the user has completed a portion of thedefined path. If the user has completed a portion of the defined path,the method continues to block 930.

At block 930, the method determines whether the completed portionincludes a segment that qualifies for calibration. In one embodiment, asegment of the path qualifies for calibration if the user's exact routethrough the segment of the path can be determined to a high degree ofaccuracy. Such a determination may be made, for example, by laying GPSsensor data over a map, and determining that a user traveled over aphysically discernible path such as a trail. In one embodiment, asegment of the path qualifies for calibration if a user traveled overthe segment at a consistent pace (e.g., at a regular cadence). Forexample, if the user ran over the path at a relatively continuous paceof between 8 and 9 minutes per mile, then the segment may qualify forcalibration. In one embodiment, a segment qualifies for calibration ifthe segment is at a consistent grade or slope (e.g., if the segment isflat). In one embodiment, a segment qualifies for calibration if itmeets all of the above mentioned criteria, or alternatively if it meetssome of the above mentioned criteria. Other criteria may also be used.If no segments of the completed portion qualify for calibration, themethod returns to block 925. If a segment of the completed portionqualifies for calibration, the method proceeds to block 935.

At block 935, a first path length is determined for the qualifyingsegment based on accelerometer data. At block 940, a second path lengthis determined for the qualifying segment based on location measurementdata (e.g., based on GPS sensor data).

At block 945, the method determines whether there is a differencebetween the first path length and the second path length. If there is nodifference in the path lengths, the method ends. If there is adifference in the path lengths, the method proceeds to block 950.

At block 950, the method calibrates a stride length correlation modelbased on the location measurement data and the accelerometer data. Thestride length correlation model may be calibrated as discussed abovewith reference to FIG. 1. The method then ends.

FIG. 10 illustrates a block diagram of a machine in the exemplary formof a computer system within which a set of instructions, for causing themachine to perform any one or more of the methodologies discussedherein, may be executed. FIG. 1 is one embodiment of a computer systemthat may be used with the present invention. It will be apparent tothose of ordinary skill in the art, however that other alternativesystems of various system architectures may also be used.

Returning to FIG. 10, a data processing system includes a bus or otherinternal communication means 1015 for communicating information, and aprocessor 1010 coupled to the bus 1015 for processing information. Thesystem further comprises a random access memory (RAM) or other volatilestorage device 1050 (referred to as memory), coupled to bus 1015 forstoring information and instructions to be executed by processor 1010.Main memory 1050 also may be used for storing temporary variables orother intermediate information during execution of instructions byprocessor 1010. The system also comprises a read only memory (ROM)and/or static storage device 1020 coupled to bus 1015 for storing staticinformation and instructions for processor 1010, and a data storagedevice 1025 such as a magnetic disk or optical disk and itscorresponding disk drive. Data storage device 1025 is coupled to bus1015 for storing information and instructions.

The system may further be coupled to a display device 1070, such as acathode ray tube (CRT) or a liquid crystal display (LCD) coupled to bus1015 through bus 1065 for displaying information to a computer user. Analphanumeric input device 1075, including alphanumeric and other keys,may also be coupled to bus 1015 through bus 1065 for communicatinginformation and command selections to processor 1010. An additional userinput device is cursor control device 1080, such as a mouse, atrackball, stylus, or cursor direction keys coupled to bus 1015 throughbus 1065 for communicating direction information and command selectionsto processor 1010, and for controlling cursor movement on display device1070.

Another device, which may optionally be coupled to computer system 1000,is a communication device 1090 for accessing other nodes of adistributed system via a network. The communication device 1090 mayinclude any of a number of commercially available networking peripheraldevices such as those used for coupling to an Ethernet, token ring,Internet, or wide area network. The communication device 1090 mayfurther be a null-modem connection, or any other mechanism that providesconnectivity between the computer system 1000 and the outside world.Note that any or all of the components of this system illustrated inFIG. 10 and associated hardware may be used in various embodiments ofthe present invention.

It will be appreciated by those of ordinary skill in the art that anyconfiguration of the system may be used for various purposes accordingto the particular implementation. The control logic or softwareimplementing the present invention can be stored in main memory 1050,mass storage device 1025, or other storage medium locally or remotelyaccessible to processor 1010.

It will be apparent to those of ordinary skill in the art that thesystem, method, and process described herein can be implemented assoftware stored in main memory 1050 or read only memory 1020 andexecuted by processor 1010. This control logic or software may also beresident on an article of manufacture comprising a computer readablemedium having computer readable program code embodied therein and beingreadable by the mass storage device 1025 and for causing the processor1010 to operate in accordance with the methods and teachings herein.

The present invention may also be embodied in a handheld or portabledevice containing a subset of the computer hardware components describedabove. For example, the handheld device may be configured to containonly the bus 1015, the processor 1010, and memory 1050 and/or 1025. Thehandheld device may also be configured to include a set of buttons orinput signaling components with which a user may select from a set ofavailable options. The handheld device may also be configured to includean output apparatus such as a liquid crystal display (LCD) or displayelement matrix for displaying information to a user of the handhelddevice. Conventional methods may be used to implement such a handhelddevice. The implementation of the present invention for such a devicewould be apparent to one of ordinary skill in the art given thedisclosure of the present invention as provided herein.

The present invention may also be embodied in a special purposeappliance including a subset of the computer hardware componentsdescribed above. For example, the appliance may include a processor1010, a data storage device 1025, a bus 1015, and memory 1050, and onlyrudimentary communications mechanisms, such as a small touch-screen thatpermits the user to communicate in a basic manner with the device. Ingeneral, the more special-purpose the device is, the fewer of theelements need be present for the device to function. In some devices,communications with the user may be through a touch-based screen, orsimilar mechanism.

It will be appreciated by those of ordinary skill in the art that anyconfiguration of the system may be used for various purposes accordingto the particular implementation. The control logic or softwareimplementing the present invention can be stored on any machine-readablemedium locally or remotely accessible to processor 1010. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine (e.g. acomputer). For example, a machine readable medium includes read-onlymemory (ROM), random access memory (RAM), magnetic disk storage media,optical storage media, flash memory devices, electrical, optical,acoustical or other forms of propagated signals (e.g. carrier waves,infrared signals, digital signals, etc.).

The following detailed description of embodiments of the invention makesreference to the accompanying drawings in which like references indicatesimilar elements, showing by way of illustration specific embodiments ofpracticing the invention. Description of these embodiments is insufficient detail to enable those skilled in the art to practice theinvention. One skilled in the art understands that other embodiments maybe utilized and that logical, mechanical, electrical, functional andother changes may be made without departing from the scope of thepresent invention. The following detailed description is, therefore, notto be taken in a limiting sense, and the scope of the present inventionis defined only by the appended claims.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. A mobile apparatus comprising: an inertial sensor to monitoraccelerations; a motion processor to identify a user activity based onthe accelerations and to make a first estimation of a user activitystatistic associated with the user activity based on the accelerations,the user activity statistic being a distance traveled, the motionprocessor further to calibrate a stride length correlation model basedon the accelerations and the second estimation, wherein the stridelength correlation model is used to make the first estimation; alocation based sensor to obtain location information; a locationprocessor to make a second estimation of the user activity statisticbased on the location information; and a calculating logic to calculatethe user activity statistic based on the first estimation and the secondestimation, wherein the calculated user activity statistic is a resultof a weighted combination of the first estimation and the secondestimation.
 2. The mobile apparatus of claim 1, further comprising: atransmitter to transmit the accelerations and the location informationto an external computing device; and a receiver to receive an externallycalculated user activity statistic from the external computing device.3. The mobile apparatus of claim 2, wherein the accelerations and thelocation information are transmitted to the external computing deviceafter the user activity ends.
 4. The mobile apparatus of claim 3,wherein the externally calculated user activity statistic includes areconstruction of an event that started when the user activity wasinitially detected and ended when the user activity ceased to bedetected using the accelerations and the location information.
 5. Themobile apparatus of claim 2, wherein the user activity statistic is oneof a distance traveled, a route traveled, a speed of travel, a currentposition or a periodic human motion count.
 6. The mobile apparatus ofclaim 2, wherein calculating the user activity statistic comprises:determining an first accuracy of the first estimation and a secondaccuracy of the second estimation; weighting the first estimation andthe second estimation based on the first accuracy and the secondaccuracy; and combining the weighted first estimation and the weightedsecond estimation.
 7. The mobile apparatus of claim 2, furthercomprising: the calculating logic to verify the user activity based onthe location information.
 8. The mobile apparatus of claim 2, furthercomprising: The calculating logic to verify current location informationbased on the accelerations and past location information.
 9. The mobileapparatus of claim 2, further comprising: The location processor toautomatically trigger a preconfigured action If the location informationindicates that a user is at a particular location.
 10. The mobileapparatus of claim 1, wherein the stride length correlation model iscalibrated if the first estimation and the second estimation do notmatch, and if an accuracy of the second estimation is high.
 11. A mobiledevice comprising: an inertial sensor to receive movement data; alocation based sensor to receive location data; a calculating logic tocalculate a user activity statistic based on a weighted combination of afirst estimate based on the movement data and a second estimate based onthe location data; the calculating logic further to determine an firstaccuracy of the first estimation and a second accuracy of the secondestimation, weight the first estimation and the second estimation basedon the first accuracy and the second accuracy, and combine the weightedfirst estimation and the weighted second estimation.
 12. The mobiledevice of claim 11 further comprising: a motion processor to identify auser activity based on the movement data; the motion processor toestimate a user activity statistic for the identified user activity,based on the movement data.
 13. The mobile device of claim 11, furthercomprising: a location processor to making a second estimation of theuser activity statistic based on location information obtained by alocation based sensor.
 14. The mobile device of claim 11, the methodfurther comprising: a transmitter to transmit the accelerations and thelocation information to an external computing device and receiving thefirst estimate and the second estimate the external computing device.15. The mobile device of claim 14, wherein the accelerations and thelocation information are transmitted to the external computing deviceafter the user activity ends.
 16. The mobile device of claim 11, whereinan estimated user activity statistic includes a reconstruction of anevent that started when the user activity was initially detected andended when the user activity ceased to be detected using theaccelerations and the location information.
 17. The mobile device ofclaim 11, wherein the user activity statistic is one of a distancetraveled, a route traveled, a speed of travel, a current position or aperiodic human motion count.
 18. The mobile device of claim 11, whereincalculating the second accuracy of the second estimation comprisesverifying a current location information based on the accelerations andpast location information.
 19. A mobile device comprising: an inertialsensor to receive movement data; a location based sensor to receivelocation data; a calculating logic to calculate a user activitystatistic based on a weighted combination of a first estimate based onthe movement data and a second estimate based on the location datawherein the user activity statistic is a distance traveled; and acalibration logic to utilize a stride length correlation model based onthe accelerations and the second estimation, wherein the stride lengthcorrelation model is used to make the first estimation.
 20. The mobiledevice of claim 19, further comprising: the calculating logic further todetermine an first accuracy of the first estimation and a secondaccuracy of the second estimation, weight the first estimation and thesecond estimation based on the first accuracy and the second accuracy,and combine the weighted first estimation and the weighted secondestimation.
 21. The mobile device of claim 19, further comprising:wherein calculating the second accuracy of the second estimationcomprises verifying a current location information based on theaccelerations and past location information.
 22. The mobile device ofclaim 19, wherein an estimated user activity statistic includes areconstruction of an event that started when the user activity wasinitially detected and ended when the user activity ceased to bedetected using the accelerations and the location information.
 23. Amobile device comprising: an inertial sensor to receive movement data; alocation based sensor to receive location data; a transmitter totransmit the movement data and the location data to an externalcomputing device; and a user interface to provide information to a userof the mobile device based on a user activity statistic, calculatedbased on a weighted combination of a first estimate based on themovement data and a second estimate based on the location data; whereinan estimated user activity statistic includes a reconstruction of anevent that started when the user activity was initially detected andended when the user activity ceased to be detected using theaccelerations and the location information.
 24. The mobile device ofclaim 23, further comprising: a calculating logic to calculate the useractivity statistic based on the weighted combination of the firstestimate based on the movement data and the second estimate based on thelocation data.
 25. The mobile device of claim 23, wherein the externaldevice calculates of the first estimate based on the movement data andthe second estimate based on the location data.
 26. The mobile device ofclaim 23 further comprising: a motion processor to identify a useractivity based on the movement data; the motion processor to estimate auser activity statistic for the identified user activity, based on themovement data.
 27. The mobile device of claim 23, further comprising: alocation processor to making a second estimation of the user activitystatistic based on location information obtained by a location basedsensor.
 28. The mobile device of claim 23, wherein the accelerations andthe location information are transmitted to the external computingdevice after the user activity ends.
 29. The mobile device of claim 23,wherein the user activity statistic is one of a distance traveled, aroute traveled, a speed of travel, a current position or a periodichuman motion count.
 30. A mobile device comprising: an inertial sensorto receive movement data; a location based sensor to receive locationdata; and a user interface to provide information to a user of themobile device based on a user activity statistic, calculated based on aweighted combination of a first estimate based on the movement data anda second estimate based on the location data; wherein the weightedcombination is calculated by determining a first accuracy of the firstestimation and a second accuracy of the second estimation, weighting thefirst estimation and the second estimation based on the first accuracyand the second accuracy, and combining the weighted first estimation andthe weighted second estimation.
 31. The mobile device of claim 30,wherein calculating the second accuracy of the second estimationcomprises verifying a current location information based on theaccelerations and past location information.
 32. The mobile device ofclaim 30 further comprising: a motion processor to identify a useractivity based on the movement data; the motion processor to estimate auser activity statistic for the identified user activity, based on themovement data.
 33. The mobile device of claim 30, further comprising: alocation processor to making a second estimation of the user activitystatistic based on location information obtained by a location basedsensor.
 34. The mobile device of claim 30, the method furthercomprising: a transmitter to transmit the accelerations and the locationinformation to an external computing device and receiving the firstestimate and the second estimate the external computing device.
 35. Themobile device of claim 30, wherein the accelerations and the locationinformation are transmitted to the external computing device after theuser activity ends.
 36. The mobile device of claim 30, wherein anestimated user activity statistic includes a reconstruction of an eventthat started when the user activity was initially detected and endedwhen the user activity ceased to be detected using the accelerations andthe location information.
 37. The mobile device of claim 30, wherein theuser activity statistic is one of a distance traveled, a route traveled,a speed of travel, a current position or a periodic human motion count.38. A mobile device comprising: an inertial sensor to receive movementdata; a location based sensor to receive location data; a transmitter totransmit the movement data and the location data to an externalcomputing device; a user interface to provide information to a user ofthe mobile device based on a user activity statistic, calculated basedon a weighted combination of a first estimate based on the movement dataand a second estimate based on the location data, wherein the useractivity statistic is a distance traveled; and a calibration logic toutilize a stride length correlation model based on the accelerations andthe second estimation, wherein the stride length correlation model isused to make the first estimation.